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31. 发明申请

US20100100407A1 SCALING OPTIMIZATION OF ALLOCATION OF ONLINE ADVERTISEMENT INVENTORY 审中-公开
标题翻译：在线广告库存分配优化
公开(公告)号：US20100100407A1
公开(公告)日：2010-04-22
申请号：US12253377
申请日：2008-10-17
申请人： Long-Ji Lin , Danny Zhang
发明人： Long-Ji Lin , Danny Zhang
IPC分类号： G06Q10/00
CPC分类号： G06Q10/04 , G06Q30/02 , G06Q30/0244
摘要： A method for scaling inventory allocation includes mapping attributes to impressions through index tables; constructing a flow network of nodes each containing impressions of corresponding attributes projected to be available during a time period, contracts each including specific requests for impressions that satisfy a demand profile, and arcs to connect the nodes to the contracts that match the demand profiles of the contracts; sampling the arcs that flow into each contract at a sampling rate chosen to reduce the number of arcs to a fraction of the original arcs when the plurality of impressions that satisfy the contract is above a threshold number, the nodes corresponding to the sampled arcs being sampled nodes; and optimally allocating impressions from the sampled nodes to the contracts during the time period by solving the flow network with a minimum-cost network flow algorithm that maximizes delivery of the impressions from the sampled nodes to the contracts in a way that satisfies the corresponding demand profiles.
摘要翻译：缩放库存分配的方法包括通过索引表将属性映射到展示; 构建每个节点的流网络，每个节点包含在一段时间段内预期可用的相应属性的展示，每个包含满足需求简档的印象的特定请求的合同以及将节点连接到符合需求简档的合同的弧合约; 以满足该合同的多个印象高于阈值数目的选择以将弧数减少到原始弧的一部分的采样速率对流入每个合同的弧进行采样，对应于采样的弧的节点被采样节点; 并且通过以满足相应需求简档的方式最大化从采样节点到合同的展开最大化的最小成本网络流算法来解决流网络，从而在时间段内将采样节点的展示次数最佳地分配给契约。

32. 发明授权

US5850470A Neural network for locating and recognizing a deformable object 失效
标题翻译：用于定位和识别可变形物体的神经网络
公开(公告)号：US5850470A
公开(公告)日：1998-12-15
申请号：US521176
申请日：1995-08-30
申请人： Sun-Yuan Kung , Shang-Hung Lin , Long-Ji Lin , Ming Fang
发明人： Sun-Yuan Kung , Shang-Hung Lin , Long-Ji Lin , Ming Fang
IPC分类号： G06K9/00 , G07C9/00 , G06E1/00 , G06E3/00 , G06K9/62
CPC分类号： G06K9/6281 , G06K9/00241 , G06K9/32 , G06K9/6273 , G06K9/6278 , G07C9/00158
摘要： A system for automatically detecting and recognizing the identity of a deformable object such as a human face, within an arbitrary image scene. The system comprises an object detector implemented as a probabilistic DBNN, for determining whether the object is within the arbitrary image scene and a feature localizer also implemented as a probabilistic DBNN, for determining the position of an identifying feature on the object such as the eyes. A feature extractor is coupled to the feature localizer and receives coordinates sent from the feature localizer which are indicative of the position of the identifying feature and also extracts from the coordinates information relating to other features of the object such as the eyebrows and nose, which are used to create a low resolution image of the object. A probabilistic DBNN based object recognizer for determining the identity of the object receives the low resolution image of the object inputted from the feature extractor to identify the object.
摘要翻译：一种用于在任意图像场景内自动检测和识别诸如人脸之类的可变形对象的身份的系统。该系统包括实现为概率DBNN的对象检测器，用于确定对象是否在任意图像场景内，并且特征定位器也被实现为概率DBNN，用于确定诸如眼睛的对象上的识别特征的位置。特征提取器耦合到特征定位器并且接收从特征定位器发送的坐标，其指示识别特征的位置，并且还从坐标中提取关于诸如眉毛和鼻子的对象的其他特征的信息，这些信息是用于创建对象的低分辨率图像。用于确定对象的身份的基于概率DBNN的对象识别器接收从特征提取器输入的对象的低分辨率图像以识别对象。

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